数值模式的预报策略和方法研究进展

数值预报经历了半个多世纪的发展,已成为当前主要的客观预报工具。在模式和资料状况给定的情况下,预报效果的改善很大程度上依赖于所采用的预报策略和方法。为此,全面回顾了国内外基于数值模式的预报策略和方法研究进展,认为采取统计—动力相结合、从历史资料中提炼信息的预报策略是提高数值预报水平的可行之路。最后在总结前人工作基础上,着重介绍了动力相似预报策略和方法的相关研究,特别是实际预报中的试验情况。     

论动力相似预报的物理基础问题

为了更好地发展动力相似预报的策略和方法,基于非线性动力学成果,综合探讨了动力相似预报的物理基础问题。分析表明,“相似预报—低频流型—可预报性”在物理上存在密切联系,这在统计相似预报和动力相似预报问题中均适用。通过类比集合预报,并对实际大气不同尺度模式预报的个例分析,初步证实了相似初值对应的动力预报结果及其预报误差行为具有相似性。在此基础上,进一步提出三类相似性问题,并归纳了动力相似预报的物理基础：在初始状态相似性持续的情况下,动力预报结果以及预报误差行为具有相似性。     

Review and Prospect of Global Operational Ocean Forecasting

The marine environmental forecast plays an increasingly important role in economic growth and infrastructure development, and touches upon many fields and aspects, including marine security, energy resources development and protection, ocean shipping and fisheries. Global Ocean Data Assimilation Experiment (GODAE) OceanView supports the national research groups providing them with coordination and technical support among the partners. Forecasting centers develop and establish global operational ocean forecast systems. The global operational ocean forecasting system uses the ocean dynamic numerical model as the dynamic framework, and the near real-time high-quality observation input field is integrated into the model by data assimilation to realize the future environmental forecasts of the marine conditions covering the multi-time scale. The products are routinely validated with observations in order to assess their quality. This paper briefly introduced and reviewed the development process and current situation of the global ocean forecasting system covered by GODAE OceanView, and outlined the future development of global ocean forecasting.     

Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system

The recent improvement of numerical weather prediction (NWP) models has a strong potential for extending the lead time of precipitation and subsequent flooding. However, uncertainties inherent in precipitation outputs from NWP models are propagated into hydrological forecasts and can also be magnified by the scaling process, contributing considerable uncertainties to flood forecasts. In order to address uncertainties in flood forecasting based on single-model precipitation forecasting, a coupled atmospheric-hydrological modeling system based on multi-model ensemble precipitation forecasting is implemented in a configuration for two episodes of intense precipitation affecting the Wangjiaba sub-region in Huaihe River Basin, China. The present study aimed at comparing high-resolution limited-area meteorological model Canadian regional mesoscale compressible community model (MC2) with the multiple linear regression integrated forecast (MLRF), covering short and medium range. The former is a single-model approach; while the latter one is based on NWP models [(MC2, global environmental multiscale model (GEM), T213L31 global spectral model (T213)] integrating by a multiple linear regression method. Both MC2 and MLRF are coupled with Chinese National Flood Forecasting System (NFFS), MC2-NFFS and MLRF-NFFS, to simulate the discharge of the Wangjiaba sub-basin. The evaluation of the flood forecasts is performed both from a meteorological perspective and in terms of discharge prediction. The encouraging results obtained in this study demonstrate that the coupled system based on multi-model ensemble precipitation forecasting has a promising potential of increasing discharge accuracy and modeling stability in terms of precipitation amount and timing, along with reducing uncertainties in flood forecasts and models. Moreover, the precipitation distribution of MC2 is more problematic in finer temporal and spatial scales, even for the high resolution simulation, which requests further research on storm-scale data assimilation, sub-grid-scale parameterization of clouds and other small-scale atmospheric dynamics.     

Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area,China

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area.     

Progress in Researches on Ensemble Forecasting of Extreme Weather Based on Numerical Models

Under the background of climate change, extreme weather events (e.g., heavy rainfall, heat wave, and cold damage) in China have been occurring more frequently with an increasing trend of induced meteorological disasters. Therefore, it is of great importance to carry out research on forecasting of extreme weather. This paper systematically reviewed the primary methodology of extreme weather forecast, current status in development of ensemble weather forecasting based on numerical models and their applications to forecast of extreme weather, as well as progress in approaches for correcting ensemble probabilistic forecast. Nowadays, the forecasting of extreme weather has been generally dominated by methodology using dynamical models. That is to say, the dynamical forecasting methods based on ensemble probabilistic forecast information have become prevailing in current operational extreme weather forecast worldwide. It can be clearly found that the current major directions of research and development in this field are the application of ensemble forecasts based on numerical models to forecasting of extreme weather, and its improvement through bias correction of ensemble probabilistic forecast. Based on a relatively comprehensive review in this paper, some suggestions with respect to development of extreme weather forecast in future were further given in terms of the issues of how to propose effective approaches on improving level of identification and forecasting of extreme events.     

降水临近预报及其在水文预报中的应用研究进展

随着全球气候变化、自然变迁及陆表生境改变,极端天气频发且呈现出多尺度时空变异特征,对其进行预报和预警一直是气象水文领域关注的焦点。临近预报可较准确地预报未来短时间天气显著变化,是当前预报强降水等极端事件的主要手段。从基于天气雷达0~3 h外推临近预报、融合数值模式0~6 h临近预报的发展历程梳理了临近预报的研究进展,阐述了雷达外推算法的发展进程、雷达外推预报与数值模式预报融合技术进展,指出"取长补短"的0~6 h融合预报在提高降水预报精度、延长降水预见期等多方面有较大的发展潜力,进一步探寻及提升融合技术是未来融合预报发展的核心。将临近预报以气象水文耦合的方式引入水文预报是从源头提高水文预报精度、保障水文预报效果的主要途径,总结了现阶段主流耦合方式、空间尺度匹配技术、水文模型不确定等陆气耦合中的关键问题,阐述了外推临近预报、融合临近预报作为水文预报输入的研究进展,明确了融合临近预报在延长洪水预见期、提高洪水预报精度中存在优势,并讨论了未来的研究重点及发展方向。     

中国近海海洋锋和锋面预报研究进展

海洋锋是水文要素特性不同的2个水团之间的狭窄过渡带,是一种重要的中尺度海洋现象,对渔业、军事和海洋环境保护等许多领域有重要影响,已经成为近年来物理海洋学以及海洋交叉学科中的重要课题之一。中国近海由于众多环流、水团和涡旋在此交汇,海洋锋现象十分显著,近年来,我国学者对中国近海的海洋锋产生机制和变化规律取得了一定的进展。首先从实测水文资料分析、遥感资料分析和数值模拟3个方面分别回顾了黄海、东海、南海北部等中国近海海域海洋锋的研究进展。对于海洋锋的预报研究,我国起步较晚,但目前对近海海洋锋预报的需求十分迫切。回顾总结了国外海洋锋预报的进展,集中介绍了黑潮锋、冰岛—法罗群岛锋、湾流锋和墨西哥湾锋面涡旋的预报方法和现状,最后对如何开展中国近海海洋锋预报提出思考和展望。     

气候变化条件下青藏铁路沿线多年冻土概率预报(I):活动层厚度与地温

对1961-2100年IPCC气候模拟与预测结果进行降尺度处理,得到铁路沿线空间分辨率为1km、时间分辨率为1h的大气边界条件.对铁路和公路沿线钻孔资料在垂直和水平两个方向进行空间差值处理,得到水平1 km、垂直0.1m分辨率的沿线地下含水（冰）量的二维分布,作为初始条件.考虑气候模型预测误差和空间格网内地形的变化,以...     

数值天气预报检验方法研究进展

数值天气预报检验是改进及应用数值模式的重要环节。近年来,模式检验中的观念不断更新,适用于不同预报产品及不同用户需求的模式检验方法也不断涌现。首先简单回顾了以列联表为基础的传统的模式检验方法。其次重点总结了伴随高分辨率数值预报而出现的空间诊断检验技术,按照检验目的的不同,诊断方法可以归纳为:①基于滤波技术的分辨模式在不同时空尺度上预报能力的邻域法、尺度分离法;②利用位移偏差诊断模式预报位置、面积、方位、轴角等与观测差异的属性判别法、变形评估法。然后阐述了集合样本成员的概率分布函数(PDF)、集合预报与观测概率分布函数相似程度、事件发生的概率预报等集合预报检验方法。最后论述了空间诊断技术、集合预报检验方法的适用领域,并讨论了模式检验中存在的一些问题及未来的发展方向。     

Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches

Landslide displacement is widely obtained to discover landslide behaviors for purpose of event forecasting. This article aims to present a comparative study on landslide nonlinear displacement analysis and prediction using computational intelligence techniques. Three state-of-art techniques, the support vector machine (SVM), the relevance vector machine (RVM), and the Gaussian process (GP), are comparatively presented briefly for modeling landslide displacement series. The three techniques are discussed comparatively for both fitting and predicting the landslide displacement series. Two landslides, the Baishuihe colluvial landslide in China Three Georges and the Super-Sauze mudslide in the French Alps, are illustrated. The results prove that the computational intelligence approaches are feasible and capable of fitting and predicting landslide nonlinear displacement. The Gaussian process, on the whole, performs better than the support vector machine, relevance vector machine, and simple artificial neural network (ANN) with optimized parameter values in predictive analysis of the landslide displacement.     

Development and Application of the Chinese Global Operational Oceanography Forecasting System

Chinese Global operational Oceanography Forecasting System (CGOFS) is configured in three levels of nested grids from global ocean, open ocean to offshore. This global operational oceanography forecasting system architecture is firstly bulit in China by the National Marine Environmental Forecasting Center (NMEFC). It has been put into operational forecasting at NMEFC, providing real-time forecasting of multi-scale ocean current, temperature, salinity, wave, sea surface wind, etc. All the ocean forecasting products are released in many ways and made available through the online, realizing full-range coverage in resolution from hundreds kilometer to several kilometer. The CGOFS includes 8 subsystems: global sea-surface wind numerical forecasting subsystem, global ocean circulation numerical forecasting subsystem, global ocean wave numerical forecasting subsystem, global tide and tidal current forecasting subsystem, Indian Ocean marine environment numerical forecasting subsystem, polar sea ice numerical forecasting subsystem, refined marine environment numerical forecasting for China’s surrounding waters,and integration management subsystem for operational support service of the CGOFS. Operational applications of the CGOFS are closely connected with China’s economic-social development and military security needs. For example, the CGOFS palys a crucial role in environmental forecasting for Chinese research vessel and icebreaker Xuelong, MH370 Searching, submersible “Jiaolong” exploration and nuclear contaminant transport from Fukushima Daiichi nuclear power plant, providing important scientific support for developing an ocean power, protecting national maritime rights, ensuring marine safety and coping with ocean problems in emergency.     

Track prediction of Indian Ocean cyclones using Lagrangian advection model

A new model has been developed for track prediction of Indian Ocean cyclones. The model utilizes environmental steering flow using the forecasts from a high-resolution global model and the effect due to earth??s rotation (the beta-effect) to determine the future movement of cyclone. A new approach based on vertical profile of potential vorticity is used to determine weights for different vertical levels for computation of mean steering current. Despite the fact that the model is based on the dynamical framework, the operational cost and time for running the model is only a fraction of what is needed by a normal numerical weather prediction model. This new approach will enhance flexibility in defining the initial position of the cyclone in the model, and also, it is possible to create a large ensemble of predicted tracks to assess the impact of the uncertainty of initial cyclone position on the predicted tracks. The performance of the model for ten cyclones, viz. GONU (02?C08 Jun, 2007), SIDR (11?C16 November, 2007), NARGIS (27 Apr?C04 May, 2008), RASHMI (25?C27 October, 2008) KHAI-MUK (14?C16 November, 2008), NISHA (25?C27 November, 2008), SEVEN (04?C08 December, 2008), BIJLI (14?C18 April, 2009), AILA (23?C26 May, 2009), and PHYAN (09?C11 November, 2009), have been tested in the present study. The forecast errors of the present model have been computed with respect to the Joint Typhoon Warning Center best track analysis positions. The forecast skill improvement (mean of ten cyclones) of the model with respect to the Climatology and Persistence (CLIPER) statistical model varies from 7 to 67?% between 12 and 72?h.     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

Evaluations and Ensemble Approaches of Western-Pacific Subtropical High and South-Asian High Ensemble Forecasting in GRAPES-GEPS

Based on the operational standard indices, the prediction skills of the Western-Pacific Subtropical High (WPSH) and South-Asian High (SAH) using 2019 real-time forecasts derived from the Global Ensemble Prediction System of GRAPES (GRAPES-GEPS) in China Meteorological Administration (CMA) Numerical Prediction Center were evaluated and the effects of different ensemble approaches on the prediction skills of WPSH and SAH indices were further investigated in this study. The results show that for WPSH, the GRAPES-GEPS has its highest prediction skill for the ridge line index, considerably high skill for the intensity and area indices, but relatively low skill for the western boundary index, and for SAH, it has comparatively high skill for the intensity and center latitude indices, but relatively lower skill for the center longitude index. Prediction errors of the GRAPES-GEPS for the WPSH forecasts are featured by the weaker intensity and area and the more eastward center position, compared with the observation, which can be effectively reduced by employing the maximum/minimum approach from ensemble members, relative to the ensemble mean approach. By direct comparison, prediction errors of the GRAPES-GEPS for the SAH forecasts are featured by the weaker intensity and the more southward center position, which tends to be slightly reduced using the ensemble mean approach. Finally, for the extreme forecast, the maximum approach provides superior performance for both WPSH and SAH than the ensemble mean approach, which can be validated in terms of the two extreme cases. These results clearly indicate that the maximum approach could better improve the GRAPES-GEPS performance for the extreme forecasting of the two primary circulation patterns than the traditional ensemble mean approach.     

Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR

In this work, the impact of assimilation of conventional and satellite data is studied on the prediction of two cyclonic storms in the Bay of Bengal using the three-dimensional variational data assimilation (3D-VAR) technique. The FANOOS cyclone (December 6?C10, 2005) and the very severe cyclone NARGIS (April 28?CMay 2, 2008) were simulated with a double-nested weather research and forecasting (WRF-ARW) model at a horizontal resolution of 9?km. Three numerical experiments were performed using the WRF model. The back ground error covariance matrix for 3DVAR over the Indian region was generated by running the model for a 30-day period in November 2007. In the control run (CTL), the National Centers for Environmental Prediction (NCEP) global forecast system analysis at 0.5° resolution was used for the initial and boundary conditions. In the second experiment called the VARCON, the conventional surface and upper air observations were used for assimilation. In the third experiment (VARQSCAT), the ocean surface wind vectors from quick scatterometer (QSCAT) were used for assimilation. The CTL and VARCON experiments have produced higher intensity in terms of sea level pressure, winds and vorticity fields but with higher track errors. Assimilation of conventional observations has meager positive impact on the intensity and has led to negative impact on simulated storm tracks. The QSCAT vector winds have given positive impact on the simulations of intensity and track positions of the two storms, the impact is found to be relatively higher for the moderate intense cyclone FANOOS as compared to very severe cyclone NARGIS.     

Prediction of pile capacity from stress-wave measurements: Some numerical aspects

The analysis of measured stress waves for the prediction of static pile capacity is described. The usual method of using the measured velocity record as the input boundary condition in a wave equation analysis with a finite element model, if not properly implemented, can lead to significant numerical errors. An alternative scheme using an accurate prescribed displacement procedure with the measured displacement record (obtained from the integration of the velocity) is proposed, and is shown to be very efficient. These aspects of the numerical modelling procedure are illustrated by a case study.     

水文集合预报试验及其研究进展

水文集合预报是一种既可以给出确定性预报值,又能提供预报值的不确定性信息的概率预报方法。简述了水文集合预报试验(Hydrologic Ensemble Prediction Experiment,HEPEX)国际计划的主要研究内容,回顾了HEPEX研究进展,分析了对水文预报发展有重要意义的3个HEPEX前沿研究:降尺度研究、集合预报系统研究以及不确定性研究。研究表明,动力-统计降尺度法和高分辨率"单一"模式及低分辨率集合相结合是HEPEX未来研究的方向。     

Modeling Ocean Currents Through Complex Random Fields Indexed in Time

Surface ocean currents are often of interest in environmental monitoring. These vectorial data can be reasonably treated as a finite realization of a complex-valued random field, where the decomposition in modulus (current speed) and direction (current direction) of the current field is natural. Moreover, when observations are also available for different time points (other than at several locations), it is useful to evaluate the evolution of their complex correlation over time (rather than in space) and the corresponding modeling which is required for estimation purposes. This paper illustrates a first approach where the temporal profile of surface ocean currents is considered. After introducing the fundamental aspects of the complex formalism of a random field indexed in time, a new class of models suitable for including the temporal component is proposed and applied to describe the time-varying complex covariance function of current data. The analysis concerns ocean current observations, taken hourly on 30 April 2016 through high frequency radar systems at some stations located in the Northeastern Caribbean Sea. The selected complex covariance model indexed in time is used for estimation purposes and its reliability is confirmed by a numerical analysis.